The ability of a substance to induce a toxicological response is better understood by analyzing the response profile over a broad range of concentrations (or doses) rather than by evaluating effects that occur at a single concentration (or dose). In vitro qHTS assays are multiple-concentration experiments that play an important role in NTPs efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a predominantly predictive science based on broad inclusion of target-specific, mechanism-based, biological observations. The analysis of qHTS data has largely been motivated by the conservative focus of pharmaceutical applications (i.e., minimizing the risk of Type I error) and generally has relied on heuristics rather than statistical tests to make activity calls. To evaluate the activity within qHTS studies, we developed a two-stage decision tree statistical model and applied it to normalized concentration-response data from twelve cell-based agonist nuclear receptor assays (AR, ER, FXR, GR, LXR, PPARd, PPARg, RXR, TRb, VDR, PXR-human, PXR-rat), and agonist assays for p53 and ELG1. We also applied the model to nuclear receptor antagonist assays (AR, FXR, GR, LXR, PPARd, PPARg, RXR, TRb, VDR) and cytotoxicity assays from various chicken cell lines. In the first stage, data obtained from 1408 substances were fit to a four-parameter Hill equation and an overall F-test comparing the best fit to the Hill equation and a horizontal line (no response) for each substance exhibiting at least 25% efficacy, using different significance thresholds, identified active compounds within the tested concentration range (5 x 10-10 M to 10-4 M). In the second stage, compounds not detected as active in the first stage were evaluated by comparing the distribution of measured responses to a control value, at an alpha value of 0.05. Using this approach, we identified a greater number of active compounds in each assay than a previously utilized heuristic approach.